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因果森林 (Causal Forest) 是由 Wager and Athey [2017] 提出的异质因果效应估计算法。该算法本质上可以理解成通过子采样生成大量不同的因果树，然后取这些树的平均。 类似于随机森林算法，因果森林可以被理解成一种近邻匹配算法，但是用数据驱动的方法来确定样本之间的相似度。
== 介绍 ==
== 算法 ==
....
== 算法实现 ==
Python 包 econml 和 R 包 grf 都有实现该算法。
== 参考文献 ==
=== Recursive Partitioning for Heterogeneous Causal Effects - arXiv ===
''[Submitted on 5 Apr 2015 (v1), last revised 30 Dec 2015 (this version, v3)]''
=== Estimation and Inference of Heterogeneous Treatment Effects ... ===
''[Submitted on 14 Oct 2015 (v1), last revised 10 Jul 2017 (this version, v4)]''
=== Generalized Random Forests - arXiv ===
''[Submitted on 5 Oct 2016 (v1), last revised 5 Apr 2018 (this version, v4)]''
=== Machine Learning Methods Economists Should Know About ===
''[Submitted on 24 Mar 2019]''
== 编者推荐 ==